Jun Jason Zhang

MIMO Radar with Frequency Diversity

We propose a new multiple-input, multiple-output (MIMO) radar system with colocated antennas that can greatly reduce target fluctuations. This is achieved by employing frequency diversity. The proposed MIMO radar system results in a radar array with frequency-division multiplexing that can also incorporate beamforming to design the transmission beam pattern. After presenting an overview of existing MIMO radar systems and their related signal models, we provide a detailed description of the proposed frequency diversity technique and the corresponding transmitter and receiver structures. Furthermore, we present a maximum likelihood estimation algorithm that incorporates frequency diversity with MIMO radar, derive the corresponding Cramér-Rao lower bound, and demonstrate its improved performance using numerical results.

Creating the Feedback Loop: Closed-Loop Neurostimulation

Current DBS therapy delivers a train of electrical pulses at set stimulation parameters. This open-loop design is effective for movement disorders, but therapy may be further optimized by a closed loop design. The technology to record biosignals has outpaced our understanding of their relationship to the clinical state of the whole person. Neuronal oscillations may represent or facilitate the cooperative functioning of brain ensembles, and may provide critical information to customize neuromodulation therapy. This review addresses advances to date, not of the technology per se, but of the strategies to apply neuronal signals to trigger or modulate stimulation systems.

Multipath exploitationwith adaptivewaveform design for tracking in urban terrain

We integrate multipath exploitation with adaptive waveform design in order to increase the tracking performance of a vehicle moving in urban terrain. Mitigation of both clutter and strong multipath returns can result in increased target detection. However, exploiting multiple bounces from obstacles such as buildings can be shown to increase radar coverage and scene visibility, especially in the absence of direct line-of-sight paths. For this purpose, we formulate the multipath propagation of an arbitrary number of specular bounces in urban terrain for three-dimensional motion. We then further exploit and optimize multipath returns by dynamically selecting the parameters of the transmitted waveform to minimize the predicted mean-squared tracking error. We demonstrate our proposed approach in a realistic urban environment by varying the type of measurement to include regions of obscuration and different number of multipath bounces.

Fault Detection, Identification, and Location in Smart Grid Based on Data-Driven Computational Methods

A fault detection, identification, and location approach is proposed and studied in this paper. This approach is based on matching pursuit decomposition (MPD) using Gaussian atom dictionary, hidden Markov model (HMM) of real-time frequency and voltage variation features, and fault contour maps generated by machine learning algorithms in smart grid (SG) systems. Specifically, the time-frequency features are extracted by MPD from the frequency and voltage signals, which are sampled by the frequency disturbance recorders in SG. A hybrid clustering algorithm is then developed and used to cluster the frequency and voltage signal features into various symbols. Using the symbols, two detection HMMs are trained for fault detection to distinguish between normal and abnormal SG operation conditions. Also, several identification HMMs are trained under different system fault scenarios, and if a fault occurs, the trained identification HMMs are used to identify different fault types. In the meantime, if the fault is detected by the detection HMMs, a fault contour map will be generated using the feature extracted by the MPD from the voltage signals and topology information of SG. The numerical results demonstrate the feasibility, effectiveness, and accuracy of the proposed approach for the diagnosis of various types of faults with different measurement signal-to-noise ratios in SG systems.

Where does AlphaGo go: from church-turing thesis to AlphaGo thesis and beyond

An investigation on the impact and significance of the AlphaGo vs. Lee Sedol Go match is conducted, and concludes with a conjecture of the AlphaGo Thesis and its extension in accordance with the Church-Turing Thesis in the history of computing. It is postulated that the architecture and method utilized by the AlphaGo program provide an engineering solution for tackling issues in complexity and intelligence. Specifically, the AlphaGo Thesis implies that any effective procedure for hard decision problems such as NP-hard can be implemented with AlphaGo-like approach. Deep rule-based networks are proposed in attempt to establish an understandable structure for deep neural networks in deep learning. The success of AlphaGo and corresponding thesis ensure the technical soundness of the parallel intelligence approach for intelligent control and management of complex systems and knowledge automation.

Synchrophasor-Based Auxiliary Controller to Enhance the Voltage Stability of a Distribution System With High Renewable Energy Penetration

Wind energy is highly location-dependent. Many desirable wind resources in North America are located in rural areas without direct access to the transmission grid. By connecting megawatt-scale wind turbines to the distribution system, the cost of building transmission facilities can be avoided and wind power supplied to consumers can be greatly increased; however, integrating megawatt-scale wind turbines on distribution feeders will impact the distribution feeder stability, especially voltage stability. Distributed wind turbine generators (WTGs) have the capability to aid in grid stability if equipped with appropriate controllers, but few investigations are focusing on this. This paper investigates the potential of using real-time measurements from distribution phasor measurement units for a new WTG control algorithm to stabilize the voltage deviation of a distribution feeder. This paper proposes a novel auxiliary coordinated-control approach based on a support vector machine (SVM) predictor and a multiple-input and multiple-output model predictive control on linear time-invariant and linear time-variant systems. The voltage condition of the distribution system is predicted by the SVM classifier using synchrophasor measurement data. The controllers equipped on WTGs are triggered by the prediction results. The IEEE 13-bus distribution system with WTGs is used to validate and evaluate the proposed auxiliary control approach.

Efficient Bayesian Tracking of Multiple Sources of Neural Activity: Algorithms and Real-Time FPGA Implementation

We propose new Bayesian algorithms to automatically track current dipole sources of neural activity in real time. We integrate multiple particle filters to track the dynamic parameters of a known number of dipole sources, resulting in reducing the computational intensity incurred due to the large number of sensors required to observe magnetoencephalography (MEG) or electroencephalography (EEG) measurements. When we also need to estimate the time-varying number of dipole sources, we develop an algorithm based on applying probability hypothesis density filtering (PHDF) for multiple object tracking. The PHDF is implemented using particle filters (PF-PHDF), and it is applied in a closed-loop with MEG/EEG measurements to first estimate the number of sources and then their corresponding amplitude, location and orientation. The PF-PHDF tracking algorithm uses an online, window-based multiple channel decomposition processing approach that reduces the overall processing time and computational complexity. We demonstrate the improved performances of the proposed algorithms by simulating neural activity tracking systems witWe propose new Bayesian algorithms to automatically track current dipole sources of neural activity in real time. We integrate multiple particle filters to track the dynamic parameters of a known number of dipole sources, resulting in reducing the computational intensity incurred due to the large number of sensors required to observe magnetoencephalography (MEG) or electroencephalography (EEG) measurements. When we also need to estimate the time-varying number of dipole sources, we develop an algorithm based on applying probability hypothesis density filtering (PHDF) for multiple object tracking. The PHDF is implemented using particle filters (PF-PHDF), and it is applied in a closed-loop with MEG/EEG measurements to first estimate the number of sources and then their corresponding amplitude, location and orientation. The PF-PHDF tracking algorithm uses an online, window-based multiple channel decomposition processing approach that reduces the overall processing time and computational complexity. We demonstrate the improved performances of the proposed algorithms by simulating neural activity tracking systems with both synthetic and real data. We map the proposed algorithms onto Xilinx Virtex-5 field-programmable gate array (FPGA) platforms and demonstrate real-time tracking performance. For example, our results showed that the PF-PHDF algorithm can process 100 data samples from three dipoles in only 5.1 ms, when 3 dipole sources are present.h both synthetic and real data. We map the proposed algorithms onto Xilinx Virtex-5 field-programmable gate array (FPGA) platforms and demonstrate real-time tracking performance. For example, our results showed that the PF-PHDF algorithm can process 100 data samples from three dipoles in only 5.1 ms, when 3 dipole sources are present.

A new parallel implementation for particle filters and its application to adaptive waveform design

Sequential Monte Carlo particle filters (PFs) are useful for estimating nonlinear non-Gaussian dynamic system parameters. As these algorithms are recursive, their real-time implementation can be computationally complex. In this paper, we analyze the bottlenecks in existing parallel PF algorithms, and we propose a new approach that integrates parallel PFs with independent Metropolis-Hastings (PPF-IMH) algorithms to improve root mean-squared estimation error performance. We implement the new PPF-IMH algorithm on a Xilinx Virtex-5 field programmable gate array (FPGA) platform. For a one-dimensional problem and using 1,000 particles, the PPF-IMH architecture with four processing elements utilizes less than 5% Virtex-5 FPGA resources and takes 5.85 μs for one iteration. The algorithm performance is also demonstrated when designing the waveform for an agile sensing application.

Time-Frequency Characterization and Receiver Waveform Design for Shallow Water Environments

We investigate a frequency-domain characterization of shallow water environments based on normal-mode models of acoustic mediums. The shallow water environment can be considered as a time-dispersive system whose time-varying impulse response can be expressed as a superposition of time-frequency components with dispersive characteristics. After studying the dispersive characteristics, a blind time-frequency processing technique is employed to separate the normal-mode components without knowledge of the environment parameters. This technique is based on first approximating the time-frequency structure of the received signal and then designing time-frequency separation filters based on warping techniques. Following this method, we develop two types of receivers to exploit the diversity inherent in the shallow water environment model and to improve underwater communication performance. Numerical results demonstrate the dispersive system characterization and the improved processing performance of the receiver structures.

A Short-Term and High-Resolution Distribution System Load Forecasting Approach Using Support Vector Regression With Hybrid Parameters Optimization

This paper proposes an approach for distribution system load forecasting, which aims to provide highly accurate short-term load forecasting with high resolution utilizing a support vector regression (SVR) based forecaster and a two-step hybrid parameters optimization method. Specifically, because the load profiles in distribution systems contain abrupt deviations, a data normalization is designed as the pretreatment for the collected historical load data. Then an SVR model is trained by the load data to forecast the future load. For better performance of SVR, a two-step hybrid optimization algorithm is proposed to determine the best parameters. In the first step of the hybrid optimization algorithm, a designed grid traverse algorithm (GTA) is used to narrow the parameters searching area from a global to local space. In the second step, based on the result of the GTA, particle swarm optimization is used to determine the best parameters in the local parameter space. After the best parameters are determined, the SVR model is used to forecast the short-term load deviation in the distribution system. The performance of the proposed approach is compared to some classic methods in later sections of this paper.